The diverse spectrum of material characteristics including band gap, mechanical moduli, color, phonon and electronic density of states, along with catalytic and surface properties are intricately intertwined with the atomic structure and the corresponding interatomic bond-lengths. This interconnection extends to the manifestation of interplanar spacings within a crystalline lattice. Analysis of these interplanar spacings and the comprehension of any deviations, whether it be lattice compression or expansion, commonly referred to as strain, hold paramount significance in unraveling various unknowns within the field. Transmission Electron Microscopy (TEM) is widely used to capture atomic-scale ordering, facilitating direct investigation of interplanar spacings. However, creating critical contour maps for visualizing and interpreting lattice stresses in TEM images remains a challenging task. Here we developed a Python code for TEM image processing that can handle a wide range of materials including nanoparticles, 2D materials, pure crystals and solid solutions. This algorithm converts local differences in interplanar spacings into contour maps allowing for a visual representation of l
Artificial Intelligence (AI) has rapidly evolved over the past decade and has advanced in areas such as language comprehension, image and video recognition, programming, and scientific reasoning. Recent AI technologies based on large language models and foundation models are approaching or surpassing artificial general intelligence. These systems demonstrate superior performance in complex problem solving, natural language processing, and multi-domain tasks, and can potentially transform fields such as science, industry, healthcare, and education. However, these advancements have raised concerns regarding the safety and trustworthiness of advanced AI, including risks related to uncontrollability, ethical conflicts, long-term socioeconomic impacts, and safety assurance. Efforts are being expended to develop internationally agreed-upon standards to ensure the safety and reliability of AI. This study analyzes international trends in safety and trustworthiness standardization for advanced AI, identifies key areas for standardization, proposes future directions and strategies, and draws policy implications. The goal is to support the safe and trustworthy development of advanced AI and e
In this study, we propose a novel approach for investigating the formation of solid electrolyte interphase (SEI) in Na-ion batteries (NIB) through the coupling of in situ liquid electrochemical transmission electron microscopy (ec-TEM) and gas-chromatography mass-spectrometry (GC/MS). To optimize this coupling, we conducted experiments on the sodiation of hard carbon materials (HC) using two different setups: in situ ec-TEM holder (operating in an "anode free" configuration, referred to as $μ$-battery) and ex-situ setup (Swagelok battery configuration). In the in situ TEM experiments, we intentionally degraded the electrolyte (NP30) using cyclic voltammetry (CV) and analyzed the recovered liquid product using GC/MS, while the solid product ($μ$-chip) was analyzed using TEM techniques in a post-mortem analysis. The ex-situ experiments served as a reference to observe and detect the insertion of Na+ ions in the HC, SEI size (389 nm), SEI composition (P, Na, F, and O), and Na plating. Furthermore, the TEM analysis revealed a cyclability limitation in our in situ TEM system. This issue appears to be caused by the deposition of Na in the form of a "foam" structure, resulting from the ga
With ever-increasing data volumes, it is essential to develop automated approaches for identifying nanoscale defects in transmission electron microscopy (TEM) images. However, compared to features in conventional photographs, nanoscale defects in TEM images exhibit far greater variation due to the complex contrast mechanisms and intricate defect structures. These challenges often result in much less labeled data and higher rates of annotation errors, posing significant obstacles to improving machine learning model performance for TEM image analysis. To address these limitations, we examined transfer learning by leveraging large, pre-trained models used for natural images. We demonstrated that by using the pre-trained encoder and L2-regularization, semantically complex features are ignored in favor of simpler, more reliable cues, substantially improving the model performance. However, this improvement cannot be captured by conventional evaluation metrics such as F1-score, which can be skewed by human annotation errors treated as ground truth. Instead, we introduced novel evaluation metrics that are independent of the annotation accuracy. Using grain boundary detection in UO2 TEM ima
Impacts of isotope ion mass on trapped electron mode (TEM) driven turbulence and zonal flows in magnetically confined fusion plasmas are investigated. Gyrokinetic simulations of TEM-driven turbulence in three-dimensional magnetic configuration of LHD plasmas with hydrogen isotope ions and real-mass kinetic electrons are realized for the first time, and the linear and the nonlinear nature of the isotope and collisional effects on the turbulent transport and zonal-flow generation is clarified. It is newly found that combined effects of the collisional TEM stabilization by the isotope ions and the associated increase in the impacts of the steady zonal flows at the near-marginal linear stability lead to the significant transport reduction with the opposite ion mass dependence in comparison to the conventional gyro-Bohm scaling. The universal nature of the isotope effects on the TEM-driven turbulence and zonal flows is verified for a wide variety of toroidal plasmas, e.g., axisymmetric tokamak and non-axisymmetric helical/stellarator systems.
Modelling, simulation and optimization form an integrated part of modern design practice in engineering and industry. Tremendous progress has been observed for all three components over the last few decades. However, many challenging issues remain unresolved, and the current trends tend to use nature-inspired algorithms and surrogate-based techniques for modelling and optimization. This 4th workshop on Computational Optimization, Modelling and Simulation (COMS 2013) at ICCS 2013 will further summarize the latest developments of optimization and modelling and their applications in science, engineering and industry. In this review paper, we will analyse the recent trends in modelling and optimization, and their associated challenges. We will discuss important topics for further research, including parameter-tuning, large-scale problems, and the gaps between theory and applications.
Background: Large language models have demonstrated strong performance on general medical examinations, but subspecialty clinical reasoning remains challenging due to rapidly evolving guidelines and nuanced evidence hierarchies. Methods: We evaluated January Mirror, an evidence-grounded clinical reasoning system, against frontier LLMs (GPT-5, GPT-5.2, Gemini-3-Pro) on a 120-question endocrinology board-style examination. Mirror integrates a curated endocrinology and cardiometabolic evidence corpus with a structured reasoning architecture to generate evidence-linked outputs. Mirror operated under a closed-evidence constraint without external retrieval. Comparator LLMs had real-time web access to guidelines and primary literature. Results: Mirror achieved 87.5% accuracy (105/120; 95% CI: 80.4-92.3%), exceeding a human reference of 62.3% and frontier LLMs including GPT-5.2 (74.6%), GPT-5 (74.0%), and Gemini-3-Pro (69.8%). On the 30 most difficult questions (human accuracy less than 50%), Mirror achieved 76.7% accuracy. Top-2 accuracy was 92.5% for Mirror versus 85.25% for GPT-5.2. Conclusions: Mirror provided evidence traceability: 74.2% of outputs cited at least one guideline-tier so
In this study, the energy exchange between electrons and ions in ITG TEM turbulence is investigated using gyrokinetic simulations. The energy exchange in TEM turbulence is primarily composed of the cooling of electrons associated with perpendicular drift and the heating of ions moving parallel to magnetic field lines. TEM turbulence facilitates energy transfer from electrons to ions, which is opposite to the direction observed in ITG turbulence. In mixed ITG TEM turbulence, the relative magnitudes of parallel heating and perpendicular cooling for each species determine the overall direction and magnitude of energy exchange. From the viewpoint of entropy balance, it is further confirmed that energy flows from the species with larger entropy production, caused by particle and heat fluxes, to the other species in ITG TEM turbulence. The predictability of turbulent energy exchange in ITG-TEM turbulence by the quasilinear model is examined. In addition, an alternative method based on the correlation between energy flux and energy exchange is developed, and its validity is demonstrated.
Motion prediction, recently popularized as world models, refers to the anticipation of future agent states or scene evolution, which is rooted in human cognition, bridging perception and decision-making. It enables intelligent systems, such as robots and self-driving cars, to act safely in dynamic, human-involved environments, and informs broader time-series reasoning challenges. With advances in methods, representations, and datasets, the field has seen rapid progress, reflected in quickly evolving benchmark results. Yet, when state-of-the-art methods are deployed in the real world, they often struggle to generalize to open-world conditions and fall short of deployment standards. This reveals a gap between research benchmarks, which are often idealized or ill-posed, and real-world complexity. To address this gap, this survey revisits the generalization and deployability of motion prediction models, with an emphasis on applications of robotics, autonomous driving, and human motion. We first offer a comprehensive taxonomy of motion prediction methods, covering representations, modeling strategies, application domains, and evaluation protocols. We then study two key challenges: (1) h
New Mobile technologies have created a new social dimension where individuals can develop increased levels of their social awareness by keeping in touch with old friends, making new friends, dispense new data or product, and getting information in many more aspects of everyday lives, making one to become more knowledgeable which is very beneficial especially for students. Social networks, in particular enable users to share and discuss common interests and provide infrastructures for integrating various user experiences: synchronous and asynchronous communication, game-playing, sharing links and files. The trend of using social networks and social media to deliver and exchange knowledge could bring a new era of social learning in which learners make use all four language skills of reading, writing, listening and speaking. Unlike a traditional e-leaning paradigm with pre-defined curriculum and standard textbooks, social knowledge could be aggregated on demand, just in time, and in context of engaging challenges from social networks, making learning more exciting, social and, game-like experience. Social learning environment engages the learners in discussion, collaboration, explorat
We investigated six presolar grains from very primitive regions of the matrix in the unequilibrated ordinary chondrite Semarkona with TEM. These grains include one SiC, one oxide (Mg-Al spinel), and four silicates. Structural and elemental compositional studies of presolar grains located within their meteorite hosts have the potential to provide information on conditions and processes throughout the grains' histories. Our analyses show that the SiC and spinel grains are stoichiometric and well crystallized. In contrast, the majority of the silicate grains are non-stoichiometric and poorly crystallized. These findings are consistent with previous TEM studies of presolar grains from interplanetary dust particles and chondritic meteorites. We interpret the poorly crystalline nature, non-stoichiometry, more Fe- rather than Mg-rich compositions, and/or compositional heterogeneities as features of the formation by condensation under non-equilibrium conditions. Evidence for parent body alteration includes rims with mobile elements (S or Fe) on the SiC grain and one silicate grain. Other features characteristic of secondary processing in the interstellar medium, the solar nebula, and/or on
The observation of distinct peaks in tokamak core reflectometry measurements - named quasi-coherent-modes (QCMs) - are identified as a signature of Trapped-Electron-Mode (TEM) turbulence [H. Arnichand et al. 2016 Plasma Phys. Control. Fusion 58 014037]. This phenomenon is investigated with detailed linear and nonlinear gyrokinetic simulations using the \gene code. A Tore-Supra density scan is studied, which traverses through a Linear (LOC) to Saturated (SOC) Ohmic Confinement transition. The LOC and SOC phases are both simulated separately. In the LOC phase, where QCMs are observed, TEMs are robustly predicted unstable in linear studies. In the later SOC phase, where QCMs are no longer observed, ITG modes are identified. In nonlinear simulations, in the ITG (SOC) phase, a broadband spectrum is seen. In the TEM (LOC) phase, a clear emergence of a peak at the TEM frequencies is seen. This is due to reduced nonlinear frequency broadening of the underlying linear modes in the TEM regime compared with the ITG regime. A synthetic diagnostic of the nonlinearly simulated frequency spectra reproduces the features observed in the reflectometry measurements. These results support the identifi
In the contemporary world, with the incubation of advanced technologies and tremendous outbursts of research works, analyzing big data to incorporate research strategies becomes more helpful using the tools and techniques presented in the current research scenario. This paper indeed tries to tackle the most prominent challenges relating to big data analysis by utilizing a text mining approach to analyze research data published in the field of production management as a case to begin with. The study has been conducted by considering research data of International Journal of Production Research (IJPR) indexed in Scopus between 1961-2017 by dividing the analysis incurred into 3 fragments being 1961-1990, 1991-2010 and finally 2011-2017 as a case to highlight the focus of journal. This has indeed provided multi-faceted benefits such as increasing the effectiveness of the procured data with well-established comparisons between R and Python Programming along with providing detailed research trends on the research work incubated. The results of the study highlighted some most prominent topics in the existing IJPR literature such as system's optimization, supplier selection, process design
Recent improvements in large language models (LLMs) have had a dramatic effect on capabilities and productivity across many disciplines involving critical thinking and writing. The development of the model context protocol (MCP) provides a way to extend the power of LLMs to a specific set of tasks or scientific equipment with help from curated tools and resources. Here, we describe a framework called TEM Agent designed for transmission electron microscopy (TEM) that leverages the benefits of LLMs through a MCP approach. We simultaneously access and control several subsystems of the TEM, a data management platform, and high performance computing resources through text-based instructions. We demonstrate the abilities of the TEM Agent to set up and complete intricate workflows using a simplified set of MCP tools and resources accompanying a commercial LLM without any additional training. The use of a framework such as the TEM Agent simplifies access to complex microscope ecosystems comprised of several vendor and custom systems enhancing the ability of users to accomplish microscopy experiments across a range of difficulty levels.
Transmission electron microscope (TEM) images are often corrupted by noise, hindering their interpretation. To address this issue, we propose a deep learning-based approach using simulated images. Using density functional theory calculations with a set of pseudo-atomic orbital basis sets, we generate highly accurate ground truth images. We introduce four types of noise into these simulations to create realistic training datasets. Each type of noise is then used to train a separate convolutional neural network (CNN) model. Our results show that these CNNs are effective in reducing noise, even when applied to images with different noise levels than those used during training. However, we observe limitations in some cases, particularly in preserving the integrity of circular shapes and avoiding visible artifacts between image patches. To overcome these challenges, we propose alternative training strategies and future research directions. This study provides a valuable framework for training deep learning models for TEM image denoising.
Measuring and forecasting opinion trends from real-time social media is a long-standing goal of big-data analytics. Despite its importance, there has been no conclusive scientific evidence so far that social media activity can capture the opinion of the general population. Here we develop a method to infer the opinion of Twitter users regarding the candidates of the 2016 US Presidential Election by using a combination of statistical physics of complex networks and machine learning based on hashtags co-occurrence to develop an in-domain training set approaching 1 million tweets. We investigate the social networks formed by the interactions among millions of Twitter users and infer the support of each user to the presidential candidates. The resulting Twitter trends follow the New York Times National Polling Average, which represents an aggregate of hundreds of independent traditional polls, with remarkable accuracy. Moreover, the Twitter opinion trend precedes the aggregated NYT polls by 10 days, showing that Twitter can be an early signal of global opinion trends. Our analytics unleash the power of Twitter to uncover social trends from elections, brands to political movements, and
We present a case for developing a millikelvin-temperature transmission electron microscope (TEM). We start by reviewing known reasons for such development, then present new possibilities that have been opened up by recent progress in superconducting quantum circuitry, and finally report on our ongoing experimental effort. Specifically, we first review possibilities to observe a quantum mechanically superposed electromagnetic field around a superconducting qubit. This is followed by a new idea on TEM observation of microwave photons in an unusual quantum state in a resonator. We then proceed to review potential applications of these phenomena, which include low dose electron microscopy beyond the standard quantum limit. Finally, anticipated engineering challenges, as well as the authors' current ongoing experimental effort towards building a millikelvin TEM are described. In addition, we provide a brief introduction to superconducting circuitry in the Appendix for the interested reader who is not familiar with the subject.
Trained neural networks are promising tools to analyze the ever-increasing amount of scientific image data, but it is unclear how to best customize these networks for the unique features in transmission electron micrographs. Here, we systematically examine how neural network architecture choices affect how neural networks segment, or pixel-wise separate, crystalline nanoparticles from amorphous background in transmission electron microscopy (TEM) images. We focus on decoupling the influence of receptive field, or the area of the input image that contributes to the output decision, from network complexity, which dictates the number of trainable parameters. We find that for low-resolution TEM images which rely on amplitude contrast to distinguish nanoparticles from background, the receptive field does not significantly influence segmentation performance. On the other hand, for high-resolution TEM images which rely on a combination of amplitude and phase contrast changes to identify nanoparticles, receptive field is a key parameter for increased performance, especially in images with minimal amplitude contrast. Our results provide insight and guidance as to how to adapt neural network
We recast the Howie-Whelan equations for generating simulated transmission electron microscope (TEM) images, replacing the dependence on local atomic displacements with atomic positions only. This allows very rapid computation of simulated TEM images for arbitrarily complex atomistic configurations of lattice defects and dislocations in the dynamical two beam approximation. Large scale massively-overlapping cascade simulations performed with molecular dynamics, are used to generate representative high-dose nanoscale irradiation damage in tungsten at room temperature, and we compare the simulated TEM images to experimental TEM images with similar irradiation and imaging conditions. The simulated TEM shows 'white-dot' damage in weak-beam dark-field imaging conditions, in line with our experimental observations and as expected from previous studies, and in bright-field conditions a dislocation network is observed. In this work we can also compare the images to the nanoscale lattice defects in the original atomic structures, and find that at high dose the white spots are not only created by small dislocation loops, but rather arise from nanoscale fluctuations in strains around curved s
Small open-source medical large language models (LLMs) offer promising opportunities for low-resource deployment and broader accessibility. However, their evaluation is often limited to accuracy on medical multiple choice question (MCQ) benchmarks, and lacks evaluation of consistency, robustness, or reasoning behavior. We use MCQ coupled to human evaluation and clinical review to assess six small open-source medical LLMs (HuatuoGPT-o1 (Chen 2024), Diabetica-7B, Diabetica-o1 (Wei 2024), Meditron3-8B (Sallinen2025), MedFound-7B (Liu 2025), and ClinicaGPT-base-zh (Wang 2023)) in pediatric endocrinology. In deterministic settings, we examine the effect of prompt variation on models' output and self-assessment bias. In stochastic settings, we evaluate output variability and investigate the relationship between consistency and correctness. HuatuoGPT-o1-8B achieved the highest performance. The results show that high consistency across the model response is not an indicator of correctness, although HuatuoGPT-o1-8B showed the highest consistency rate. When tasked with selecting correct reasoning, both HuatuoGPT-o1-8B and Diabetica-o1 exhibit self-assessment bias and dependency on the order